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Machine learning-based forecasting of urban fire impact in city environments.

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Area of Science:

  • Disaster Management
  • Artificial Intelligence
  • Urban Safety

Background:

  • Effective resource allocation is critical for fire departments to manage incidents efficiently.
  • Predicting fire escalation can significantly improve response outcomes and reduce associated damages.

Purpose of the Study:

  • To develop and validate a predictive model for estimating the likelihood of fire escalation.
  • To inform fire department resource allocation strategies through data-driven insights.

Main Methods:

  • Analysis of 47,382 fire incidents using an XGBoost model.
  • Incorporation of building characteristics, temporal data, and GIS spatial features.
  • Validation through 5-fold cross-validation, temporal holdouts, and geographic tests.

Main Results:

  • The predictive model achieved 85.6% accuracy and an AUC of 0.83.
  • Key predictors for fire escalation included older buildings, nighttime/weekend incidents, building structure, use, and floor count.
  • Simulations indicated potential reductions of 25% in property damage, 21% in firefighter injuries, and 18% in response times.

Conclusions:

  • Predictive analytics offers a powerful tool for enhancing real-time firefighting efficiency and public safety.
  • The developed framework shows promise for optimizing resource allocation and incident management.
  • Further validation in diverse urban settings and with refined severity scales is recommended for broader applicability.